Measurement of contagion spatial spread probability in public places: A case study on COVID-19.

Lu Chen, Xiuyan Liu, Tao Hu, Shuming Bao, Xinyue Ye, Ning Ma, Xiaoxue Zhou

Abstract read

In one paragraph

Article in Applied geography (Sevenoaks, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

7 authors.

Lu ChenSchool of Management and Economics, Southeast University, Nanjing, 211189, China.

Xiuyan LiuSchool of Management and Economics, Southeast University, Nanjing, 211189, China.

Tao HuDepartment of Geography, Oklahoma State University, Stillwater, OK, 74075, USA.

Shuming BaoCenter for Geographic Analysis, Harvard University, Cambridge, MA, 02138, USA.

Xinyue YeDepartment of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX, 77840, USA.

Ning MaSchool of Law, Southeast University, Nanjing, 211189, China.

Xiaoxue ZhouJiangsu Institute of Industrial Development, Nanjing University of Finance and Economics, Nanjing, 210023, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The scale and scope of the COVID-19 epidemic have highlighted the need for timely control of viral transmission. This paper proposed a new spatial probability model of epidemic infection using an improved Wasserstein distance algorithm and Monte Carlo simulation. This method identifies the public places in which COVID-19 spreads and grows easily. The Wasserstein Distance algorithm is used to calculate the distribution similarity between COVID-19 cases and the public places. Further, we used hypothesis tests and Monte Carlo simulation to estimate the spatial spread probability of COVID-19 in different public places. We used Snow's data to test the stability and accuracy of this measurement. This verification proved that our method is reliable and robust. We applied our method to the detailed geographic data of COVID-19 cases and public places in Wuhan. We found that, rather than financial service institutions and markets, public buildings such as restaurants and hospitals in Wuhan are 95 percent more likely to be the public places of COVID-19 spread.

Indexed as

Covid-19Public placesSpatial analysisSpread probabilityWasserstein distance

Identifiers

PMID35418716

DOI10.1016/j.apgeog.2022.102700

PMCPMC8986488

What OpenQuestion holds

Textmetadata

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.